1. Field of the Invention
This invention relates to speech communication systems and particularly to a speech communication system utilizing adaptive recursive filters.
2. Description of the Prior Art
Many types of prior art speech communications systems have been proposed. In U.S. Pat. No. 3,750,024 (Dunn et al.) there is disclosed a system which determines redundant information in a speech to be transmitted and removes that redundant information to produce a residual signal. At least one parameter of the redundant information is also determined. This parameter and the residual signal are multiplexed for transmission. The transmitted signal is demultiplexed in a receiver, with the resultant parameter and residual signal being used to control the operation of a filter and hence the subsequent reconstruction of the speech for utilization. In this system the transmit filter uses only input samples. No feedback path is provided between the output of the transmitter and the transmit filter to modify any filter parameters. As taught in Dunn et al. the digitally converted speech information is directly processed to develop the redundant information which is subtracted from the digitally converted speech. The filter coefficients in Dunn et al. are developed by directly analyzing the speech information. More specifically, the filter coefficients are adjusted to the input signal by computing a short term correlation function from the input samples. The best fit of the filter's response to the input spectrum is obtained by minimizing the mean square value of the output signal of the transmit filter with respect to each of the weights to subsequently lead to the optimum weights. Inverse filters are used in both the transmitter and receiver of this system. No recursive filters are used in this system.
An article by Atal and Schroeder is referenced in Column 2, line 50 et seq. of Dunn et al. This article deals with a predictive quantizer system which, like that of Dunn et al., uses short term correlation in its system operation. The system in the cited article uses only output samples to drive the predictor, whereas the system of Dunn et al. uses only input samples. Neither of these systems utilizes both input and output samples in its operation.
Another approach is briefly described in Column 5, line 8 et seq. of Dunn et al., wherein a prior art system is described as monitoring the level of the prediction and comparing it to the level of the input signal. In this approach, if the level of the prediction is not less than the level of the input signal with which it is being compared, the system assumes something is wrong, and forces the prediction to zero at that time. There appear to be two ways of forcing the prediction to zero. The system can either force all filter states to zero or force all filter coefficients to zero in order to zero the prediction. However, as indicated in Column 5, lines 14-17 of Dunn et al., this operation would diminish the advantage of having the prediction in the first place. It would further act to increase the error in the final output during the time that the system is forcing the prediction to zero, since nothing would be compared to the level of the input signal at that time.
Another system is described in U.S. Pat. No. 3,745,562 (Rosenbaum). Rosenbaum teaches an analog-to-digital encoder which uses an N dimensional quantizer to generate from an input analog signal N digits of an output code for transmission. An error signal, derived from past and future inputs, is applied to a tapped delay line, the outputs of which are multiplied by a coefficient for correcting errors in the input signal. However, the error signal is not utilized to adjust filter parameters.
U.S. Pat. No. 3,715,666 (Mueller) teaches a start-up system for a transversal equalizer in which a received signal is processed by a digital filter and compared with a locally generated data stream identical to the transmitted data for generating an error signal to correct filter parameters.
None of the above-described systems teach the provision of a transmitter containing an adaptive recursive filter in a control loop simulating an adaptive recursive filter in a receiver. It should also be noted that many prior art adaptive filters used in speech coding systems basically use transversal filter structures because their convergence requirements are known. A system utilizing adaptive recursive filters would be more powerful because the recursive filter has both poles and zeros. However, no prior art has been found by applicant that could make a recursive filter adapt or that would indicate that the convergence requirements for an adaptive recursive filter was heretofore known.